Adaptive distributed learning model optimization for performance prediction under data privacy constraints

Abstract

An adaptive distributed learning model optimization for performance prediction under data privacy constraints. Specifically, the disclosed method and system introduce a framework through which a shared machine learning model deployed across a network of computing nodes may be optimized using private and decentralized datasets. Through the proposed framework, the shared machine learning model may achieve a good generalization error globally across the network, and may also achieving good predictive performance locally while employed on each computing node.

What is claimed is: 
     
         1 . A method for adaptive distributed learning model optimization, comprising:
 receiving, by a worker node and from a central node, a first learning model configured with an initial learning state;   making a first determination that a first data shift has transpired;   issuing, based on the first determination, a first data shift notice to the central node;   receiving, in response to issuing the first data shift notice, a first data shift instruction from the central node; and   adjusting, based on the first data shift instruction, the initial learning state through optimization of the first learning model using local data to obtain a second learning model configured with local data adjusted learning state.   
     
     
         2 . The method of  claim 1 , wherein making the first determination, comprises:
 generating a first local data distribution reflective of recently collected local data;   obtaining a second local data distribution reflective of historical local data;   computing a distribution distance between the first local data distribution and the second local data distribution; and   determining that the distribution distance exceeds a distribution distance threshold.   
     
     
         3 . The method of  claim 1 , further comprising:
 selecting a feature set portion of the local data; and   processing the feature set portion using the first learning model and the second learning model to respectively predict a first value of a storage array performance metric and a second value of the storage array performance metric,   wherein the second value is a more accurate prediction of the storage array performance metric than the first value.   
     
     
         4 . The method of  claim 3 , wherein the feature set portion comprises worker node storage array telemetry and worker node configuration state. 
     
     
         5 . The method of  claim 1 , further comprising:
 making a second determination that a second data shift has transpired;   issuing, based on the second determination, a second data shift notice to the central node;   receiving, in response to issuing the second data shift notice, a second data shift instruction from the central node; and   transmitting, based on the second data shift instruction, the local data adjusted learning state to the central node.   
     
     
         6 . The method of  claim 5 , wherein the first data shift instruction is received based on a data shift counter, maintained by the central node, falling short of a data shift counter threshold, wherein the second data shift instruction is received based on the data shift counter at least satisfying the data shift counter threshold. 
     
     
         7 . The method of  claim 6 , wherein the data shift counter threshold reflects a predefined percentage of a set of worker nodes in a network, wherein the set of worker nodes comprises the worker node. 
     
     
         8 . The method of  claim 5 , further comprising:
 receiving, from the central node and in response to transmitting the local data adjusted learning state, a third learning model configured with aggregated learning state,   wherein the aggregated learning state is derived from a set of local data adjusted learning states comprising the local data adjusted learning state.   
     
     
         9 . The method of  claim 8 , wherein the set of local data adjusted learning states further comprises other local data adjusted learning state transmitted to the central node by other worker nodes in a network. 
     
     
         10 . The method of  claim 9 , wherein the central node, the worker node, and the other worker nodes participate in federated learning to comply with local data privacy concerns. 
     
     
         11 . A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor on a worker node, enables the computer processor to:
 receive, from a central node, a first learning model configured with an initial learning state;   make a first determination that a first data shift has transpired;   issue, based on the first determination, a first data shift notice to the central node;   receive, in response to issuing the first data shift notice, a first data shift instruction from the central node; and   adjust, based on the first data shift instruction, the initial learning state through optimization of the first learning model using local data to obtain a second learning model configured with local data adjusted learning state.   
     
     
         12 . The non-transitory CRM of  claim 11 , comprising computer readable program code to make the first determination, which when executed by the computer processor on the worker node, enables the computer processor to:
 generate a first local data distribution reflective of recently collected local data;   obtain a second local data distribution reflective of historical local data;   compute a distribution distance between the first local data distribution and the second local data distribution; and   determine that the distribution distance exceeds a distribution distance threshold.   
     
     
         13 . The non-transitory CRM of  claim 11 , comprising computer readable program code, which when executed by the computer processor on the worker node, further enables the computer processor to:
 select a feature set portion of the local data; and   process the feature set portion using the first learning model and the second learning model to respectively predict a first value of a storage array performance metric and a second value of the storage array performance metric,   wherein the second value is a more accurate prediction of the storage array performance metric than the first value.   
     
     
         14 . The non-transitory CRM of  claim 13 , wherein the feature set portion comprises worker node storage array telemetry and worker node configuration state. 
     
     
         15 . The non-transitory CRM of  claim 11 , comprising computer readable program code, which when executed by the computer processor on the worker node, further enables the computer processor to:
 make a second determination that a second data shift has transpired;   issue, based on the second determination, a second data shift notice to the central node;   receive, in response to issuing the second data shift notice, a second data shift instruction from the central node; and   transmit, based on the second data shift instruction, the local data adjusted learning state to the central node.   
     
     
         16 . The non-transitory CRM of  claim 15 , wherein the first data shift instruction is received based on a data shift counter, maintained by the central node, falling short of a data shift counter threshold, wherein the second data shift instruction is received based on the data shift counter at least satisfying the data shift counter threshold. 
     
     
         17 . The non-transitory CRM of  claim 16 , wherein the data shift counter threshold reflects a predefined percentage of a set of worker nodes in a network, wherein the set of worker nodes comprises the worker node. 
     
     
         18 . The non-transitory CRM of  claim 17 , comprising computer readable program code, which when executed by the computer processor on the worker node, further enables the computer processor to:
 receive, from the central node and in response to transmitting the local data adjusted learning state, a third learning model configured with aggregated learning state,   wherein the aggregated learning state is derived from a set of local data adjusted learning states comprising the local data adjusted learning state.   
     
     
         19 . The non-transitory CRM of  claim 18 , wherein the set of local data adjusted learning states further comprises other local data adjusted learning state transmitted to the central node by other worker nodes in a network. 
     
     
         20 . The non-transitory CRM of  claim 19 , wherein the central node, the worker node, and the other worker nodes participate in federated learning to comply with local data privacy concerns.